Most of classic attention models belong to bottom-up mechanism. The basis of those bottom-up models can date back to Treisman and Gelade's [1]} Feature Integration Theory (FIT), where they combined important visual features to direct human attention over pop-out and conjunction search tasks. Typical attention models consist of three cascaded components: visual feature extraction, saliency inference, and saliency integration. Low-level features, e.g., intensity, color, and orientation [2]}, are first engineered by hand. Inspired
by studies that the salient regions in the visual field would first pop out through different low-level features from their surroundings, center-surround contrast is widely adopted for
inferring the saliency. Then saliency is either computed by the relative difference between a region and its local surrounding [2]}, [4]}, [5]}, or calculating global rarity of features over the entire scene [6]}, [7]}. Since saliency is computed over several features in parallel, the final step is for fusing them in a scalar map called the “saliency map”. This step is guided by different principles, e.g., pre-defined linear weights [2]}, trainable weights based on Support Vector Machine (SVM) [5]}. From the view of mechanism to obtain attention, previous models can also be classified as [10]}: cognitive model [2]}, [12]}, Bayesian model [7]}, decision theoretic model [14]}, [15]}, information theoretic model [6]}, graphical model [4]}, [18]}, spectral analysis model [19]}, pattern classification model [5]} and some other models [21]} that are based on other mechanism.
